Module 0 (optional): Good practices in Research Software
Development.
This module is based on material from The Carpentries, where you can find
a lot of free, open source and high quality material on software
development and data science.
Get started with the module here: https://github.com/neural-data-science-course/research-software-development
Module content:
- Tracking changes:
git add &
git commit
- Exploring history, checking out older versions
- Ignoring things with .gitignore files
- Github remotes
- Creating pull requests
- Review process
- Good practices for collaboration
Module 1: Neural data handling and preprocessing
Get started with the module here:
https://github.com/neural-data-science-course/neural-data
Module content:
- Introduction to the local field potential
- Fourier analysis and power spectrum
- Signal filtering
- Introduction to time-frequency analysis
- Wavelet transform and spectrograms
- Introduction to calcium imaging and CaImAn
- Data loading and summary images
- Motion correction
- Source extraction with Constrained Non-negative Matrix
Factorization
Module 2: Single cell analysis
Get started with the module here:
https://github.com/neural-data-science-course/single-cell-analysis
Module content:
- Visualization techniques for the response of a neuron
- Raster plots and Peri-timulus Time Histograms (PSTH)
- Tuning curves
- Visualizing hippocampal place cells
- Measuring spatial information
- The timulus-response function
- Linear and non-linear stages of GLMs
- Linear Gaussian models
- Linear-Nonlinear Poisson models
Module 3: Population methods
https://github.com/neural-data-science-course/population-methods
Module content:
- Introduction to bayesian decoding with poisson neurons
- Decoding position on a linear track
- Decoding during sleep
- Analysis of seuqential reactivations during sleep
- Support Vector Machines and linear separability of data
- Decoding stimulus identity from neural activity
- Cross validation techniques
- Assessing significance with surrogate data
- Principal component analysis (PCA)
- Discovering collective modes of acrtivity in the cortex
- Clustering: K-means and DBSCAN
- Discovering co-active assemblies with clustering methods